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The fastest way to improve an API is to measure the request path, identify its dominant bottleneck, and change one variable at a time. Start with p50, p95, and p99 latency, throughput, errors, saturation, payload size, cache hits, and database timing. Distributed traces then show whether time is spent in DNS, connection and TLS setup, queueing, application code, downstream calls, database work, or serialization.
Once the bottleneck is known, the highest-value changes usually fall into five groups: reuse or improve network connections, send less data, cache only data with well-defined freshness and sensitivity rules, shape database work to the endpoint, and validate every change with production-like load and rollback criteria. No general percentage improvement is promised here; the result depends on your traffic, payloads, geography, protocol support, and workload.
- Optimize the slowest span, not the most fashionable technology.
- Compare before and after at the same percentiles; a better average can hide a worse p99.
- Carry correctness, authorization, freshness, and failure behavior through every optimization.
1. Establish a baseline with percentiles and traces
Do not begin by changing queries, adding a cache, or switching protocols. First record a baseline for representative endpoints and traffic. Track p50 (the median), p95, and p99 latency, plus throughput, error rate, CPU and memory saturation, request and response sizes, cache hit rate, and database timing. Averages are useful for capacity accounting but can conceal the slow tail that users experience during contention.
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Instrument the complete path: DNS lookup, TCP connection, TLS negotiation, connection-pool wait, application queueing, handler execution, downstream calls, database queries, and response serialization. A trace that attributes 180 milliseconds to “the API” is less actionable than one showing 20 ms in the database, 90 ms waiting for a downstream service, and 70 ms in serialization.
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Application Insights, profilers, and equivalent tracing systems can expose query and code hot spots. Microsoft guidance recommends measuring the impact of high-performance approaches before accepting their added complexity. Keep the baseline queryable by endpoint, method, status code, deployment, region, client type, payload size, and cache state.
Define the optimization hypothesis
Write down the suspected bottleneck, the metric expected to move, the safety constraints, and the rollback trigger. For example: “Reducing an endpoint’s selected columns should lower serialization time and response bytes without changing authorized fields; revert if p99 or error rate rises.” This prevents an attractive but irrelevant change from being judged a success.
2. Reuse HTTP connections
Connection setup is part of API latency. Under HTTP/1.1, configure clients to use persistent connections and a correctly sized connection pool. Google Cloud advises that HTTP/1.1 clients should always reuse TCP connections; mature HTTP libraries normally manage pooling, but pool limits and idle timeouts still need to match your concurrency and server behavior.
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A pool that is too small makes requests queue behind available sockets; one that is too large creates unnecessary sockets, handshakes, and pressure on the server. Monitor pool wait time, active and idle connections, connection failures, and server-side limits. Ensure intermediaries do not silently close idle connections sooner than the client expects, and make retry logic aware that a reused connection can fail between requests.
3. Prefer HTTP/2 or HTTP/3 when the stack supports them
HTTP/2 and HTTP/3 multiplex multiple requests over a connection, reducing the need for parallel TCP handshakes. Google Cloud generally recommends HTTP/3 or HTTP/2 for better performance, reduced head-of-line blocking, and greater resilience to network failures.
Adoption is an end-to-end decision. Confirm that the client library, reverse proxy, load balancer, origin server, and observability tools negotiate and report the protocol correctly. Test behavior through every production intermediary, including protocol downgrade, connection migration, timeout handling, and load-balancer health checks. A protocol upgrade that your edge supports but your client or telemetry cannot interpret is not a complete optimization.
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4. Compress text responses
JSON, XML, CSV, and similar text formats often contain repeated structure that compresses well. Enable gzip or another encoding supported by both sides when the bandwidth saved justifies the CPU used to compress and decompress. Clients advertise support with an Accept-Encoding: gzip request header; verify that the response carries the corresponding Content-Encoding header.
Choose compression deliberately
- Measure compressed and uncompressed bytes, CPU time, and p95/p99 latency rather than assuming compression is always faster.
- Do not compress content that is already compressed, and make sure caches vary on the encoding request so one representation is not served incorrectly to another client.
- Check that error responses and streaming behavior follow the same policy as successful responses.
5. Return only the fields the client needs
Large representations cost bandwidth, parsing time, memory, and serialization CPU. Offer field selection or sparse fieldsets so a client can request only the properties it will display or store. Google performance guidance describes partial responses as a way to avoid transferring, parsing, and storing unneeded fields.
Treat field selection as an API contract. Define whether an omitted field is simply absent, represented by a default, or incompatible with a requested expansion. Apply authorization and privacy checks to every field before projection; hiding a field from the response must not bypass permission checks used to calculate it or retrieve related data. Instrument requested field sets so you can remove expensive, unused projections safely.
6. Use PATCH for partial updates
When an update changes only a few properties, a PATCH operation can send only those changes instead of retransmitting the complete representation. This reduces request bytes and server work when the API semantics permit partial updates.
Specify update semantics
Document whether PATCH merges fields, replaces a subdocument, removes a value when it is null, or follows a formal patch format. Validate the resulting resource, enforce authorization on each changed field, and define how concurrent edits are handled. ETags or another precondition can prevent a stale client from overwriting a newer update. If an endpoint actually requires a complete replacement, use a replacement method rather than disguising it as a partial update.
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7. Paginate large collections
A collection endpoint should never return an unbounded result set. A single large GET increases server processing, network traffic, memory use, and latency, as Microsoft API guidance notes.
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Build a predictable page contract
- Set a server-enforced maximum page size and allow a smaller client-requested size.
- Use a stable, documented sort order; include a unique tie-breaker so records do not move between pages.
- Prefer a continuation token for datasets that change while a client is paging. Encode enough state to resume consistently without exposing sensitive query details.
- Return an explicit next-page link or token and define what happens when the token expires.
Offset pagination can be adequate for small, stable datasets, but deep offsets often require scanning and become slower as the offset grows. Whichever method you choose, measure database time and duplicate or missing records during concurrent writes.
8. Set response and request size limits
Bound every input that can grow: request bodies, page sizes, batch counts, filter expressions, upload parts, and expansion lists. Limits protect latency and capacity as well as security. Reject an oversized request before expensive parsing or database work; during streaming, an unacceptable body can be terminated with HTTP status 413.
Make limits visible in documentation and error responses, and distinguish a client-requested limit from the server maximum. Apply separate budgets to authenticated and unauthenticated traffic only when that policy is intentional. Monitor near-limit requests so you can identify clients that need a smaller page, a batch redesign, or an asynchronous workflow.
9. Cache at the right layers
Caching can remove repeated work from the client or browser, a CDN or gateway, the application, or the database. UKHSA guidance recommends a multilayer strategy and distributed caching where appropriate; AWS API Gateway documents response caching as an optimization.
Match the layer to the audience
- Browser or client cache: useful for representations that can safely be stored on an individual device.
- CDN or gateway cache: effective for shareable responses close to users, provided cache keys and authorization vary correctly.
- Application cache: useful for expensive computations or shared lookups that are not appropriate for an edge cache.
- Database cache: can reduce repeated reads, but it does not replace efficient queries or appropriate indexes.
Use a distributed cache when multiple application instances need a consistent shared view. Define cache keys from every request attribute that changes the representation, including locale, version, authorization scope, and selected fields.
10. Design invalidation and freshness rules
Cache data that is frequently requested, rarely changed, or expensive to generate. Do not cache volatile or sensitive responses without an explicit policy for freshness, invalidation, authorization, encryption, and cache-key variation. UKHSA specifically cautions that frequently changing data needs an invalidation strategy and that client caches can expose sensitive information.
Choose a freshness model
Set a time-to-live only when its staleness window is acceptable. For writes that must be visible quickly, invalidate or update affected keys as part of the write path, and decide what happens if invalidation fails. Versioned keys, event-driven invalidation, and stale-while-revalidate can each be appropriate, but they have different failure modes. Track hit rate, stale responses, evictions, and stampedes; add request coalescing or jittered expiry when many keys can expire together.
11. Use validators for unchanged resources
For resources that have not changed, validators avoid retransmitting the representation. Send an ETag and/or Last-Modified value, and honor If-None-Match or If-Modified-Since when the resource semantics allow it. If the validator still matches, return 304 Not Modified without the response body.
Generate validators from a stable representation version, not from a value that changes on every serialization. Ensure authorization and content negotiation are reflected in the validator or cache variation; a matching tag for one representation must not incorrectly validate another. Test behavior after updates, deletes, clock skew, and concurrent writes.
12. Minimize network round trips
Every request adds connection, queueing, scheduling, and serialization overhead. When the domain permits, combine related data into one call, embed a carefully chosen sub-resource, or expose a batch endpoint with strict limits and authorization. Microsoft recommends retrieving required data in a single call when practical, and Postman REST guidance identifies embedded complex sub-resources as one way to avoid repeated requests.
Avoid making responses unbounded
Combining calls is not permission to return an entire object graph. Define which relationships can be expanded, cap expansion depth and item counts, and measure the resulting payload and database work. A batch endpoint should report per-item success or failure clearly and preserve idempotency rules. If clients have unrelated needs, separate endpoints may keep caching and authorization simpler.
13. Shape database queries to the endpoint
Push filtering, aggregation, sorting, and projection into the database rather than retrieving excess rows and processing them in application memory. Select only columns needed by the endpoint, inspect query plans, and add indexes that match real predicates and ordering.
Index for the workload
Indexes can reduce read cost but add storage, write amplification, and maintenance overhead. Base them on observed query patterns, not on every column that appears in a filter. Recheck plans after data growth, statistics changes, schema migrations, and database-version upgrades; an index that helps one endpoint can destabilize another plan.
Keep database timing in the same trace as API timing. A fast query that returns thousands of unnecessary rows may still create a slow serialization and network path, so evaluate rows examined, rows returned, lock waits, and total endpoint latency together.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.14. Eliminate N+1 and avoid unnecessary ORM work
N+1 behavior occurs when an initial query loads a collection and the application then issues one additional query per item. Microsoft warns that projection queries over collections can produce N+1 SQL queries. Inspect generated SQL and count database round trips under realistic collection sizes.
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- Batch related lookups or use a set-based join when the relationship and cardinality are understood.
- Project directly into the response shape instead of materializing full entities.
- Use read-only, no-tracking queries where the ORM supports them and change tracking is unnecessary.
- Set explicit limits on related collections so an expansion cannot multiply work without bound.
Compare the generated plan and response correctness after each change. Eager loading can fix N+1 but may create a Cartesian explosion; separate bounded batch queries can be safer for high-cardinality relationships.
15. Roll out, load-test, and re-measure
Validate an optimization with production-like payloads, concurrency, cache state, geography, and failure conditions. Compare the same p50, p95, p99, throughput, error, saturation, payload, cache, and database metrics used in the baseline. Include cold-cache and warm-cache runs; a change that looks excellent only after warming may not help a new deployment or a rarely used tenant.
Use controlled delivery
- Run repeatable load tests against a staging environment with production-shaped data and realistic downstream dependencies.
- Release behind a feature flag or to a small canary population.
- Watch tail latency, error classes, queue depth, connection-pool waits, database locks, cache evictions, and resource saturation.
- Expand exposure only when the predefined success and safety thresholds hold for the intended observation period.
- Keep a tested rollback path, including cache-key cleanup, schema compatibility, and protocol fallback where relevant.
Microsoft guidance recommends measuring the impact of high-performance approaches before accepting their complexity. Treat every optimization as a hypothesis: retain it only when the measured gain is material at the user-relevant percentile and its operational cost is understood.
How to choose among the techniques
Use the bottleneck and the failure mode to prioritize work. The table maps each technique to the decision questions that should accompany implementation.
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| Technique | Primary bottleneck addressed | Main cost or risk | Key evidence to monitor |
|---|---|---|---|
| Baseline with percentiles and traces | Unknown or misattributed latency | Instrumentation overhead and analysis time | p50/p95/p99, spans, throughput, errors, saturation |
| Reuse connections | TCP and TLS setup | Pool contention or excess open sockets | Pool wait, handshakes, idle timeouts |
| HTTP/2 or HTTP/3 | Connection multiplexing and head-of-line blocking | Proxy, client, and telemetry compatibility | Negotiated protocol, tail latency, failures |
| Compression | Text payload bandwidth | Compression CPU and cache variation | Bytes, CPU, encoding correctness |
| Field selection | Serialization and unnecessary transfer | Contract and authorization complexity | Field usage, bytes, serialization time |
| PATCH | Update request size and processing | Merge and concurrency semantics | Payload size, validation failures, conflicts |
| Pagination | Unbounded collection work | More client requests and paging consistency | Page latency, rows returned, duplicates |
| Size limits | Oversized work and resource exhaustion | Client incompatibility at the boundary | 413 responses, near-limit traffic |
| Layered caching | Repeated computation and reads | Invalidation, memory, and sensitivity risk | Hit rate, evictions, stale responses |
| Freshness and invalidation | Stale or unsafe cached data | Operational complexity and race conditions | Staleness windows, invalidation failures |
| Validators | Retransmission of unchanged data | Incorrect representation or authorization variation | 304 rate, validator mismatches |
| Fewer round trips | Per-request network and scheduling overhead | Larger responses and broader authorization | Request count, payload, expansion cost |
| Endpoint-shaped queries | Excess rows, columns, and database work | Index write overhead and plan changes | Query plans, rows examined, lock waits |
| N+1 elimination | Repeated ORM queries | Join multiplication or batch complexity | SQL count, generated SQL, collection latency |
| Load test and controlled rollout | Unknown production behavior | Test and deployment effort | Before/after percentiles, saturation, rollback triggers |
For caching, explicitly weigh invalidation and data-sensitivity risk. For compression, compare CPU with bandwidth savings. For HTTP/2 or HTTP/3, verify every intermediary. For database changes, include write overhead and plan stability. Prefer an improvement visible at p95 or p99 over one that changes only the mean, unless the mean is the metric that governs your actual capacity constraint.
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